With the development of the computer and image processing technologies, face recognition is more and more used in security systems, interactive video applications, image editing and archiving applications, and computer vision applications, etc. For example, video face recognition has been driven by its huge potential in developing applications in many domains including video surveillance security, augmented reality, automatic video tagging, medical analysis, quality control, and video-lecture assessment. Even though the face recognition is a relatively easy task for human brains, it is challenging for machines due to large variations in appearance of identified objects in terms of orientation, illumination, expression and occlusion.
One challenging problem in face recognition is deducing a subject's identity through a provided image. Research efforts have been made on addressing practical large-scale face recognition systems in uncontrolled environments. Collaborative representation-based classification has been recently thoroughly investigated in recognizing human faces given frontal views with varying expressions and illumination, as well as occlusion and disguise. Previous collaborative representation-based classifiers in Face Recognition (FR) solve a regularized regression model, with the coefficients being either sparse or not sparse, under the assumption that a face can be represented as a linear combination of training faces, as is done in Sparse Representation-Based Classification (SRC).
The main idea of SRC is that a subject's face sample can be represented as a sparse linear combination of available images of the same subject captured under different conditions (e.g., poses, lighting conditions, occlusions etc.). The same principle can also be applied when a face image is represented in a lower dimensional space describing important and easily identifiable features. SRC first codes a testing sample as a sparse linear combination of all the training samples, and then classifies the testing sample by evaluating which class leads to the minimum representation error. The regression model of SRC is formulated with dense noise and an estimated coefficient vector. The coefficient vector is a sparse vector and can be estimated by optimization methods.
Despite the success of sparse representation based classifiers, the necessity of optimization methods for improved FR rates has been criticized. Researchers attribute the enhancement in FR performance to the collaborative representation of one image using multiple similar images, rather than the effect of the optimization method regularized coefficients. Furthermore, regularization of the coefficients can only be successful under certain conditions.
Various existing face recognition schemes have proven robust in modeling the noise characteristics for a range of image corruption types. Error correction methods have been proposed in order to make regression models more robust over outliers (e.g., occlusions). In error correction methods, an auxiliary variable is added to the regression model to characterize the error which is often distributed different from Gaussian. A prior assumption of the distribution of the error is needed in order to estimate it jointly with the representation coefficients. Researchers have investigated the use of the whole structure of the error in order to characterize contiguous occlusions. The error image is assumed to be low-rank and is estimated jointly with the representation coefficients.
Nevertheless, most presented approaches are mainly tested on cases where the noise fits their error model assumptions. For example, researchers consider a low-rank regularizer for the noise term, hence being successful at recognizing faces under block-occlusion. At the same time, other researchers combine low-rankness and sparsity in order to extend their recognition rates in cases such as random pixel corruptions. However, when the percentage of corrupted pixels increases significantly, failing to satisfy the low-rank assumption, recognition rates can reduce dramatically.
Further, in face recognition, complex occlusions and variations and their combinations may occur in both the query images and the training samples. The regularization of the error with one prior distribution might not be sufficient to characterize the error. For example, if the face image has varying facial expressions or lighting variations, then low-rankness alone may not adequately describe the error, rather the error can more fittingly be described as low-rank and sparse.
The disclosed method and system are directed to solve one or more problems set forth above and other problems.